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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import os
- import cv2
- import time
- import paddle
- import numpy as np
- __all__ = [
- 'Timer',
- 'Detection',
- 'load_det_results',
- 'preprocess_reid',
- 'get_crops',
- 'clip_box',
- 'scale_coords',
- ]
- class Timer(object):
- """
- This class used to compute and print the current FPS while evaling.
- """
- def __init__(self):
- self.total_time = 0.
- self.calls = 0
- self.start_time = 0.
- self.diff = 0.
- self.average_time = 0.
- self.duration = 0.
- def tic(self):
- # using time.time instead of time.clock because time time.clock
- # does not normalize for multithreading
- self.start_time = time.time()
- def toc(self, average=True):
- self.diff = time.time() - self.start_time
- self.total_time += self.diff
- self.calls += 1
- self.average_time = self.total_time / self.calls
- if average:
- self.duration = self.average_time
- else:
- self.duration = self.diff
- return self.duration
- def clear(self):
- self.total_time = 0.
- self.calls = 0
- self.start_time = 0.
- self.diff = 0.
- self.average_time = 0.
- self.duration = 0.
- class Detection(object):
- """
- This class represents a bounding box detection in a single image.
- Args:
- tlwh (ndarray): Bounding box in format `(top left x, top left y,
- width, height)`.
- confidence (ndarray): Detector confidence score.
- feature (Tensor): A feature vector that describes the object
- contained in this image.
- """
- def __init__(self, tlwh, confidence, feature):
- self.tlwh = np.asarray(tlwh, dtype=np.float32)
- self.confidence = np.asarray(confidence, dtype=np.float32)
- self.feature = feature
- def to_tlbr(self):
- """
- Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
- `(top left, bottom right)`.
- """
- ret = self.tlwh.copy()
- ret[2:] += ret[:2]
- return ret
- def to_xyah(self):
- """
- Convert bounding box to format `(center x, center y, aspect ratio,
- height)`, where the aspect ratio is `width / height`.
- """
- ret = self.tlwh.copy()
- ret[:2] += ret[2:] / 2
- ret[2] /= ret[3]
- return ret
- def load_det_results(det_file, num_frames):
- assert os.path.exists(det_file) and os.path.isfile(det_file), \
- 'Error: det_file: {} not exist or not a file.'.format(det_file)
- labels = np.loadtxt(det_file, dtype='float32', delimiter=',')
- results_list = []
- for frame_i in range(0, num_frames):
- results = {'bbox': [], 'score': []}
- lables_with_frame = labels[labels[:, 0] == frame_i + 1]
- for l in lables_with_frame:
- results['bbox'].append(l[1:5])
- results['score'].append(l[5])
- results_list.append(results)
- return results_list
- def scale_coords(coords, input_shape, im_shape, scale_factor):
- im_shape = im_shape.numpy()[0]
- ratio = scale_factor[0][0]
- pad_w = (input_shape[1] - int(im_shape[1])) / 2
- pad_h = (input_shape[0] - int(im_shape[0])) / 2
- coords = paddle.cast(coords, 'float32')
- coords[:, 0::2] -= pad_w
- coords[:, 1::2] -= pad_h
- coords[:, 0:4] /= ratio
- coords[:, :4] = paddle.clip(coords[:, :4], min=0, max=coords[:, :4].max())
- return coords.round()
- def clip_box(xyxy, input_shape, im_shape, scale_factor):
- im_shape = im_shape.numpy()[0]
- ratio = scale_factor.numpy()[0][0]
- img0_shape = [int(im_shape[0] / ratio), int(im_shape[1] / ratio)]
- xyxy[:, 0::2] = paddle.clip(xyxy[:, 0::2], min=0, max=img0_shape[1])
- xyxy[:, 1::2] = paddle.clip(xyxy[:, 1::2], min=0, max=img0_shape[0])
- return xyxy
- def get_crops(xyxy, ori_img, pred_scores, w, h):
- crops = []
- keep_scores = []
- xyxy = xyxy.numpy().astype(np.int64)
- ori_img = ori_img.numpy()
- ori_img = np.squeeze(ori_img, axis=0).transpose(1, 0, 2)
- pred_scores = pred_scores.numpy()
- for i, bbox in enumerate(xyxy):
- if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:
- continue
- crop = ori_img[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
- crops.append(crop)
- keep_scores.append(pred_scores[i])
- if len(crops) == 0:
- return [], []
- crops = preprocess_reid(crops, w, h)
- return crops, keep_scores
- def preprocess_reid(imgs,
- w=64,
- h=192,
- mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225]):
- im_batch = []
- for img in imgs:
- img = cv2.resize(img, (w, h))
- img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
- img_mean = np.array(mean).reshape((3, 1, 1))
- img_std = np.array(std).reshape((3, 1, 1))
- img -= img_mean
- img /= img_std
- img = np.expand_dims(img, axis=0)
- im_batch.append(img)
- im_batch = np.concatenate(im_batch, 0)
- return im_batch
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